TY - GEN
T1 - Personalized ranking
T2 - 2007 ACM Symposium on Applied Computing
AU - You, Gae Won
AU - Hwang, Seung Won
PY - 2007
Y1 - 2007
N2 - As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, in the context of text retrieval, in contrast to traditional web search engines retrieving the same results for all users, major commercial search engines are starting to support personalization, improving the search quality by adapting to the user-specific retrieval contexts, e.g., prior search history or other application contexts. This paper studies how to enable such personalization in the context of structured data retrieval. In particular, we adopt context-sensitive ranking model to formalize personalization as a cost-based optimization over context-sensitive rankings collected. With this formalism, personalization is essentially retrieving the context-sensitive ranking matching the specific user's retrieval context and generating a personalized ranking accordingly. In particular, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. Our empirical evaluations over real-life data validate both the effectiveness and efficiency of our framework.
AB - As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, in the context of text retrieval, in contrast to traditional web search engines retrieving the same results for all users, major commercial search engines are starting to support personalization, improving the search quality by adapting to the user-specific retrieval contexts, e.g., prior search history or other application contexts. This paper studies how to enable such personalization in the context of structured data retrieval. In particular, we adopt context-sensitive ranking model to formalize personalization as a cost-based optimization over context-sensitive rankings collected. With this formalism, personalization is essentially retrieving the context-sensitive ranking matching the specific user's retrieval context and generating a personalized ranking accordingly. In particular, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. Our empirical evaluations over real-life data validate both the effectiveness and efficiency of our framework.
UR - http://www.scopus.com/inward/record.url?scp=35248897969&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=35248897969&partnerID=8YFLogxK
U2 - 10.1145/1244002.1244119
DO - 10.1145/1244002.1244119
M3 - Conference contribution
AN - SCOPUS:35248897969
SN - 1595934804
SN - 9781595934802
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 506
EP - 510
BT - Proceedings of the 2007 ACM Symposium on Applied Computing
PB - Association for Computing Machinery
Y2 - 11 March 2007 through 15 March 2007
ER -